Medical Certificate

This section describes how to build your custom OCR API to extract data from Medical Certificates using the API Builder. A Medical Certificate is a statement from a physician or other health care provider that attests to the result of a medical examination of a patient.

Prerequisites

You’ll need at least 20 Medical Certificate images or pdfs to train your OCR.

Define Your Medical Certificate Use Case

Using the Medical Certificate below, we’re going to define the fields we want to extract from it.

• Student Name: The name of the certificate holder
• Student Number: The student identification number
• Student School: The student's school for which the certificate was issued
• Address: The medical examination center address
• Medical center name: The name of the medical center where the examination was performed

That’s it for this example. Feel free to add any other relevant data that fits your requirement.

Deploy your API

Once you have defined the list of fields you want to extract from your Medical Certificate, head over to the platform and follow these steps:

1. Click on the Create a new API button on the right.

2. Next, fill in the basic information about the API you want to create as seen below.

3. Click on the Next button. The following page allows you to define and add your data model.

Define Your Model

There are two ways to add fields to your data model.

• Upload a JSON config file
• Manually add data

Upload a JSON Config

To add data fields using JSON config upload.

1. Copy the following JSON into a file.

{
  "problem_type": {
    "classificator": { "features": [], "features_name": [] },
    "selector": {
      "features": [
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "student_name",
          "public_name": "Student Name",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "student_number",
          "public_name": "Student Number",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "student_school",
          "public_name": "Student School",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "address",
          "public_name": "Address",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "medical_center",
          "public_name": "Medical Center",
          "semantics": "word"
        }
      ],
      "features_name": [
        "student_name",
        "student_number",
        "student_school",
        "address",
        "medical_center"
      ]
    }
  }
}

2. Click on Upload a JSON config.
3. The data model will be automatically filled.
4. Click on Create API at the bottom of the screen.

Manually Add Data

Using the interface, you can manually add each field for the data you are extracting. For this example, here are the different field configurations used:

• Student Name: type String that never contains numeric characters.
• Student Number: type Number without specifications.
• Student School: type String that never contains alpha characters.
• Address: type String without specifications.
• Medical Center: type String that never contains alpha characters.

Once you’re done setting up your data model, click the Create API button at the bottom of the screen.

Train Your Medical Certificate OCR

You’re all set! Now it's time to train your Medical Certificate deep learning model in the Training section of our API.

1. Upload one file at a time or a zip bundle of many files.
2. Click on the field input on the right, and the blue box on the left highlights all the corresponding field candidates in the document.
3. Next, click on the validate arrow for all the field inputs.
4. Once you have selected the proper box(es) for each of your fields as displayed on the right-hand side, click on the validate button located at the right-side bottom to send an annotation for the model you have created.
5. Repeat this process until you have trained 20 documents to create a trained model.

To get more information about the training phase, please refer to the Getting Started tutorial.

 

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